Overview

Bias can manifest in any part of a typical machine learning pipeline, from an unrepresentative dataset, to learned model representations, to the way in which the results are presented to the user. Errors that result from this bias can disproportionately impact some users more than others.

TensorFlow Data Validation (TFDV) is one tool you can use to analyze your data to find potential problems in your data, such as missing values and data imbalances - that can lead to Fairness disparities. The TFDV tool analyzes training and serving data to compute descriptive statistics, infer a schema, and detect data anomalies. Facets Overview provides a succinct visualization of these statistics for easy browsing. Both the TFDV and Facets are tools that are part of the Fairness Indicators.

In this lab, you use TFDV to compute descriptive statistics that provide a quick overview of the data in terms of the features that are present and the shapes of their value distributions. You use Facets Overview to visualize these statistics using the Civil Comments dataset.

Objectives

In this lab, you learn how to perform the following tasks:

• Use TFRecords to load record-oriented binary format data
• Use TFDV to generate statistics, and Facets to visualize the data
• Use the TFDV widget to answer questions
• Analyze label distribution for subset groups

Setup

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Qwiklabs using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time.
   There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.
   If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

Task 1. Launch Vertex AI Workbench instance

1. In the Google Cloud console, from the Navigation menu (), select Vertex AI.

2. Click Enable All Recommended APIs.

3. In the Navigation menu, click Workbench.

   At the top of the Workbench page, ensure you are in the Instances view.

4. Click Create New.

5. Configure the Instance:

   • Name: lab-workbench
   • Region: Set the region to
   • Zone: Set the zone to
   • Advanced Options (Optional): If needed, click "Advanced Options" for further customization (e.g., machine type, disk size).

6. Click Create.

This will take a few minutes to create the instance. A green checkmark will appear next to its name when it's ready.

7. Click OPEN JUPYTERLAB next to the instance name to launch the JupyterLab interface. This will open a new tab in your browser.

Click Check my progress to verify the objective. Launch Vertex AI Workbench instance

Task 2. Clone a course repo within your JupyterLab interface

To clone the training-data-analyst notebook in your JupyterLab instance:

Step 1

In JupyterLab, click the Terminal icon to open a new terminal.

Step 2

At the command-line prompt, type in the following command and press Enter.

git clone https://github.com/GoogleCloudPlatform/training-data-analyst

Step 3

Confirm that you have cloned the repository by double clicking on the training-data-analyst directory and ensuring that you can see its contents. The files for all the Jupyter notebook-based labs throughout this course are available in this directory.

Click Check my progress to verify the objective. Clone a course repo within your JupyterLab interface

Task 3. Feature analysis using TensorFlow Data Validation and Facets

In this task you perform feature analysis Using TensorFlow Data Validation and Facets.

1. In the notebook interface, navigate to training-data-analyst > courses > business > managingmlprojects > labs and open bias_tfdv_facets.ipynb.

2. In the Select Kernel dialog, choose Python 3 (ipykernal) (Local) from the list of available kernels.

3. In the notebook interface, click on Edit > Clear All Outputs (click on Edit, then in the drop-down menu, select Clear All Outputs).

4. Read through the notebook instructions and fill in lines marked with #TODO where you need to complete the code as needed.

Tip: To run the current cell, click the cell and press SHIFT+ENTER.

• Hints may also be provided for the tasks to guide you. Highlight the text to read the hints, which are in white text.
• To view the complete solution, navigate to training-data-analyst > courses > business > managingmlprojects > solutions and open bias_tfdv_facets.ipynb

End your lab

When you have completed your lab, click End Lab. Qwiklabs removes the resources you’ve used and cleans the account for you.

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• 5 stars = Very satisfied

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